Economically-Robust Dynamic Control of the Additive Manufacturing Cloud

Abstract

In additive manufacturing (AM), factories can quickly adjust their production to meet dynamic object demand without losing profitability, which allows them to implement less complex and more efficient supply chains. To facilitate the adoption of simplified supply chains, we propose the AM Cloud, where a set of micro-manufacturers pool their resources and offer them in an on-demand and pay-per-use basis. However, managing the AM Cloud requires setting a price for objects that incentivizes demand while guaranteeing that the manufacturers can earn a profit. It also requires finding control decisions that minimize operating costs under uncertain object demand. To address these challenges, we first propose a randomized multiobject auction that allocates printing area to the winning buyers and sets prices. We then design an online dynamic control algorithm that finds optimal decisions for the manufacturers to fulfill the object production orders from the auction winners without detailed knowledge of the system statistics. We show that the auction mechanism is truthful, individually rational, and guarantees that the manufacturers can make a profit. We also show that the solution obtained by our dynamic control algorithm is within a tight bound of the optimal one and can fulfill orders within a buyer-defined deadline.